R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(9.2
+ ,7.6
+ ,9.2
+ ,10
+ ,10.9
+ ,11.1
+ ,9.5
+ ,7.8
+ ,9.2
+ ,9.2
+ ,10
+ ,10.9
+ ,9.6
+ ,7.8
+ ,9.5
+ ,9.2
+ ,9.2
+ ,10
+ ,9.5
+ ,7.8
+ ,9.6
+ ,9.5
+ ,9.2
+ ,9.2
+ ,9.1
+ ,7.5
+ ,9.5
+ ,9.6
+ ,9.5
+ ,9.2
+ ,8.9
+ ,7.5
+ ,9.1
+ ,9.5
+ ,9.6
+ ,9.5
+ ,9
+ ,7.1
+ ,8.9
+ ,9.1
+ ,9.5
+ ,9.6
+ ,10.1
+ ,7.5
+ ,9
+ ,8.9
+ ,9.1
+ ,9.5
+ ,10.3
+ ,7.5
+ ,10.1
+ ,9
+ ,8.9
+ ,9.1
+ ,10.2
+ ,7.6
+ ,10.3
+ ,10.1
+ ,9
+ ,8.9
+ ,9.6
+ ,7.7
+ ,10.2
+ ,10.3
+ ,10.1
+ ,9
+ ,9.2
+ ,7.7
+ ,9.6
+ ,10.2
+ ,10.3
+ ,10.1
+ ,9.3
+ ,7.9
+ ,9.2
+ ,9.6
+ ,10.2
+ ,10.3
+ ,9.4
+ ,8.1
+ ,9.3
+ ,9.2
+ ,9.6
+ ,10.2
+ ,9.4
+ ,8.2
+ ,9.4
+ ,9.3
+ ,9.2
+ ,9.6
+ ,9.2
+ ,8.2
+ ,9.4
+ ,9.4
+ ,9.3
+ ,9.2
+ ,9
+ ,8.2
+ ,9.2
+ ,9.4
+ ,9.4
+ ,9.3
+ ,9
+ ,7.9
+ ,9
+ ,9.2
+ ,9.4
+ ,9.4
+ ,9
+ ,7.3
+ ,9
+ ,9
+ ,9.2
+ ,9.4
+ ,9.8
+ ,6.9
+ ,9
+ ,9
+ ,9
+ ,9.2
+ ,10
+ ,6.6
+ ,9.8
+ ,9
+ ,9
+ ,9
+ ,9.8
+ ,6.7
+ ,10
+ ,9.8
+ ,9
+ ,9
+ ,9.3
+ ,6.9
+ ,9.8
+ ,10
+ ,9.8
+ ,9
+ ,9
+ ,7
+ ,9.3
+ ,9.8
+ ,10
+ ,9.8
+ ,9
+ ,7.1
+ ,9
+ ,9.3
+ ,9.8
+ ,10
+ ,9.1
+ ,7.2
+ ,9
+ ,9
+ ,9.3
+ ,9.8
+ ,9.1
+ ,7.1
+ ,9.1
+ ,9
+ ,9
+ ,9.3
+ ,9.1
+ ,6.9
+ ,9.1
+ ,9.1
+ ,9
+ ,9
+ ,9.2
+ ,7
+ ,9.1
+ ,9.1
+ ,9.1
+ ,9
+ ,8.8
+ ,6.8
+ ,9.2
+ ,9.1
+ ,9.1
+ ,9.1
+ ,8.3
+ ,6.4
+ ,8.8
+ ,9.2
+ ,9.1
+ ,9.1
+ ,8.4
+ ,6.7
+ ,8.3
+ ,8.8
+ ,9.2
+ ,9.1
+ ,8.1
+ ,6.6
+ ,8.4
+ ,8.3
+ ,8.8
+ ,9.2
+ ,7.7
+ ,6.4
+ ,8.1
+ ,8.4
+ ,8.3
+ ,8.8
+ ,7.9
+ ,6.3
+ ,7.7
+ ,8.1
+ ,8.4
+ ,8.3
+ ,7.9
+ ,6.2
+ ,7.9
+ ,7.7
+ ,8.1
+ ,8.4
+ ,8
+ ,6.5
+ ,7.9
+ ,7.9
+ ,7.7
+ ,8.1
+ ,7.9
+ ,6.8
+ ,8
+ ,7.9
+ ,7.9
+ ,7.7
+ ,7.6
+ ,6.8
+ ,7.9
+ ,8
+ ,7.9
+ ,7.9
+ ,7.1
+ ,6.4
+ ,7.6
+ ,7.9
+ ,8
+ ,7.9
+ ,6.8
+ ,6.1
+ ,7.1
+ ,7.6
+ ,7.9
+ ,8
+ ,6.5
+ ,5.8
+ ,6.8
+ ,7.1
+ ,7.6
+ ,7.9
+ ,6.9
+ ,6.1
+ ,6.5
+ ,6.8
+ ,7.1
+ ,7.6
+ ,8.2
+ ,7.2
+ ,6.9
+ ,6.5
+ ,6.8
+ ,7.1
+ ,8.7
+ ,7.3
+ ,8.2
+ ,6.9
+ ,6.5
+ ,6.8
+ ,8.3
+ ,6.9
+ ,8.7
+ ,8.2
+ ,6.9
+ ,6.5
+ ,7.9
+ ,6.1
+ ,8.3
+ ,8.7
+ ,8.2
+ ,6.9
+ ,7.5
+ ,5.8
+ ,7.9
+ ,8.3
+ ,8.7
+ ,8.2
+ ,7.8
+ ,6.2
+ ,7.5
+ ,7.9
+ ,8.3
+ ,8.7
+ ,8.3
+ ,7.1
+ ,7.8
+ ,7.5
+ ,7.9
+ ,8.3
+ ,8.4
+ ,7.7
+ ,8.3
+ ,7.8
+ ,7.5
+ ,7.9
+ ,8.2
+ ,7.9
+ ,8.4
+ ,8.3
+ ,7.8
+ ,7.5
+ ,7.7
+ ,7.7
+ ,8.2
+ ,8.4
+ ,8.3
+ ,7.8
+ ,7.2
+ ,7.4
+ ,7.7
+ ,8.2
+ ,8.4
+ ,8.3
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7.7
+ ,8.2
+ ,8.4
+ ,8.1
+ ,8
+ ,7.3
+ ,7.2
+ ,7.7
+ ,8.2)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Yt-1'
+ ,'Yt-2'
+ ,'Yt-3'
+ ,'Yt-4')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('Y','X','Yt-1','Yt-2','Yt-3','Yt-4'),1:56))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X Yt-1 Yt-2 Yt-3 Yt-4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 9.2 7.6 9.2 10.0 10.9 11.1 1 0 0 0 0 0 0 0 0 0 0 1
2 9.5 7.8 9.2 9.2 10.0 10.9 0 1 0 0 0 0 0 0 0 0 0 2
3 9.6 7.8 9.5 9.2 9.2 10.0 0 0 1 0 0 0 0 0 0 0 0 3
4 9.5 7.8 9.6 9.5 9.2 9.2 0 0 0 1 0 0 0 0 0 0 0 4
5 9.1 7.5 9.5 9.6 9.5 9.2 0 0 0 0 1 0 0 0 0 0 0 5
6 8.9 7.5 9.1 9.5 9.6 9.5 0 0 0 0 0 1 0 0 0 0 0 6
7 9.0 7.1 8.9 9.1 9.5 9.6 0 0 0 0 0 0 1 0 0 0 0 7
8 10.1 7.5 9.0 8.9 9.1 9.5 0 0 0 0 0 0 0 1 0 0 0 8
9 10.3 7.5 10.1 9.0 8.9 9.1 0 0 0 0 0 0 0 0 1 0 0 9
10 10.2 7.6 10.3 10.1 9.0 8.9 0 0 0 0 0 0 0 0 0 1 0 10
11 9.6 7.7 10.2 10.3 10.1 9.0 0 0 0 0 0 0 0 0 0 0 1 11
12 9.2 7.7 9.6 10.2 10.3 10.1 0 0 0 0 0 0 0 0 0 0 0 12
13 9.3 7.9 9.2 9.6 10.2 10.3 1 0 0 0 0 0 0 0 0 0 0 13
14 9.4 8.1 9.3 9.2 9.6 10.2 0 1 0 0 0 0 0 0 0 0 0 14
15 9.4 8.2 9.4 9.3 9.2 9.6 0 0 1 0 0 0 0 0 0 0 0 15
16 9.2 8.2 9.4 9.4 9.3 9.2 0 0 0 1 0 0 0 0 0 0 0 16
17 9.0 8.2 9.2 9.4 9.4 9.3 0 0 0 0 1 0 0 0 0 0 0 17
18 9.0 7.9 9.0 9.2 9.4 9.4 0 0 0 0 0 1 0 0 0 0 0 18
19 9.0 7.3 9.0 9.0 9.2 9.4 0 0 0 0 0 0 1 0 0 0 0 19
20 9.8 6.9 9.0 9.0 9.0 9.2 0 0 0 0 0 0 0 1 0 0 0 20
21 10.0 6.6 9.8 9.0 9.0 9.0 0 0 0 0 0 0 0 0 1 0 0 21
22 9.8 6.7 10.0 9.8 9.0 9.0 0 0 0 0 0 0 0 0 0 1 0 22
23 9.3 6.9 9.8 10.0 9.8 9.0 0 0 0 0 0 0 0 0 0 0 1 23
24 9.0 7.0 9.3 9.8 10.0 9.8 0 0 0 0 0 0 0 0 0 0 0 24
25 9.0 7.1 9.0 9.3 9.8 10.0 1 0 0 0 0 0 0 0 0 0 0 25
26 9.1 7.2 9.0 9.0 9.3 9.8 0 1 0 0 0 0 0 0 0 0 0 26
27 9.1 7.1 9.1 9.0 9.0 9.3 0 0 1 0 0 0 0 0 0 0 0 27
28 9.1 6.9 9.1 9.1 9.0 9.0 0 0 0 1 0 0 0 0 0 0 0 28
29 9.2 7.0 9.1 9.1 9.1 9.0 0 0 0 0 1 0 0 0 0 0 0 29
30 8.8 6.8 9.2 9.1 9.1 9.1 0 0 0 0 0 1 0 0 0 0 0 30
31 8.3 6.4 8.8 9.2 9.1 9.1 0 0 0 0 0 0 1 0 0 0 0 31
32 8.4 6.7 8.3 8.8 9.2 9.1 0 0 0 0 0 0 0 1 0 0 0 32
33 8.1 6.6 8.4 8.3 8.8 9.2 0 0 0 0 0 0 0 0 1 0 0 33
34 7.7 6.4 8.1 8.4 8.3 8.8 0 0 0 0 0 0 0 0 0 1 0 34
35 7.9 6.3 7.7 8.1 8.4 8.3 0 0 0 0 0 0 0 0 0 0 1 35
36 7.9 6.2 7.9 7.7 8.1 8.4 0 0 0 0 0 0 0 0 0 0 0 36
37 8.0 6.5 7.9 7.9 7.7 8.1 1 0 0 0 0 0 0 0 0 0 0 37
38 7.9 6.8 8.0 7.9 7.9 7.7 0 1 0 0 0 0 0 0 0 0 0 38
39 7.6 6.8 7.9 8.0 7.9 7.9 0 0 1 0 0 0 0 0 0 0 0 39
40 7.1 6.4 7.6 7.9 8.0 7.9 0 0 0 1 0 0 0 0 0 0 0 40
41 6.8 6.1 7.1 7.6 7.9 8.0 0 0 0 0 1 0 0 0 0 0 0 41
42 6.5 5.8 6.8 7.1 7.6 7.9 0 0 0 0 0 1 0 0 0 0 0 42
43 6.9 6.1 6.5 6.8 7.1 7.6 0 0 0 0 0 0 1 0 0 0 0 43
44 8.2 7.2 6.9 6.5 6.8 7.1 0 0 0 0 0 0 0 1 0 0 0 44
45 8.7 7.3 8.2 6.9 6.5 6.8 0 0 0 0 0 0 0 0 1 0 0 45
46 8.3 6.9 8.7 8.2 6.9 6.5 0 0 0 0 0 0 0 0 0 1 0 46
47 7.9 6.1 8.3 8.7 8.2 6.9 0 0 0 0 0 0 0 0 0 0 1 47
48 7.5 5.8 7.9 8.3 8.7 8.2 0 0 0 0 0 0 0 0 0 0 0 48
49 7.8 6.2 7.5 7.9 8.3 8.7 1 0 0 0 0 0 0 0 0 0 0 49
50 8.3 7.1 7.8 7.5 7.9 8.3 0 1 0 0 0 0 0 0 0 0 0 50
51 8.4 7.7 8.3 7.8 7.5 7.9 0 0 1 0 0 0 0 0 0 0 0 51
52 8.2 7.9 8.4 8.3 7.8 7.5 0 0 0 1 0 0 0 0 0 0 0 52
53 7.7 7.7 8.2 8.4 8.3 7.8 0 0 0 0 1 0 0 0 0 0 0 53
54 7.2 7.4 7.7 8.2 8.4 8.3 0 0 0 0 0 1 0 0 0 0 0 54
55 7.3 7.5 7.2 7.7 8.2 8.4 0 0 0 0 0 0 1 0 0 0 0 55
56 8.1 8.0 7.3 7.2 7.7 8.2 0 0 0 0 0 0 0 1 0 0 0 56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X `Yt-1` `Yt-2` `Yt-3` `Yt-4`
0.807971 0.063175 1.410023 -0.566106 -0.308556 0.332779
M1 M2 M3 M4 M5 M6
0.201312 -0.040818 -0.231707 -0.134528 -0.088025 -0.162099
M7 M8 M9 M10 M11 t
0.067813 0.668141 -0.291632 -0.225191 0.191229 -0.004714
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.26374 -0.15135 -0.01340 0.14911 0.30773
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.807971 0.705680 1.145 0.2594
X 0.063175 0.054447 1.160 0.2532
`Yt-1` 1.410023 0.158127 8.917 7.47e-11 ***
`Yt-2` -0.566106 0.280564 -2.018 0.0507 .
`Yt-3` -0.308556 0.272636 -1.132 0.2648
`Yt-4` 0.332779 0.146386 2.273 0.0287 *
M1 0.201312 0.135867 1.482 0.1467
M2 -0.040818 0.149189 -0.274 0.7859
M3 -0.231707 0.162334 -1.427 0.1616
M4 -0.134528 0.145241 -0.926 0.3602
M5 -0.088025 0.136447 -0.645 0.5227
M6 -0.162099 0.133765 -1.212 0.2331
M7 0.067813 0.138458 0.490 0.6271
M8 0.668141 0.144020 4.639 4.07e-05 ***
M9 -0.291632 0.186162 -1.567 0.1255
M10 -0.225191 0.203813 -1.105 0.2762
M11 0.191229 0.153260 1.248 0.2198
t -0.004714 0.003499 -1.347 0.1860
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.188 on 38 degrees of freedom
Multiple R-squared: 0.971, Adjusted R-squared: 0.958
F-statistic: 74.8 on 17 and 38 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.055082493 0.110164987 0.944917507
[2,] 0.049167582 0.098335165 0.950832418
[3,] 0.025783427 0.051566853 0.974216573
[4,] 0.015103027 0.030206055 0.984896973
[5,] 0.009744222 0.019488445 0.990255778
[6,] 0.003643483 0.007286965 0.996356517
[7,] 0.001324786 0.002649571 0.998675214
[8,] 0.003107591 0.006215182 0.996892409
[9,] 0.163744455 0.327488911 0.836255545
[10,] 0.162011890 0.324023780 0.837988110
[11,] 0.128800206 0.257600411 0.871199794
[12,] 0.663627257 0.672745486 0.336372743
[13,] 0.785562295 0.428875410 0.214437705
[14,] 0.994752423 0.010495154 0.005247577
[15,] 0.982351600 0.035296800 0.017648400
> postscript(file="/var/www/html/rcomp/tmp/1j9n01258486958.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2fjhh1258486958.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/33y1e1258486958.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4fyy41258486958.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5wyl61258486958.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 56
Frequency = 1
1 2 3 4 5 6
0.073557681 -0.056262626 -0.131009956 -0.028422141 -0.161079153 0.156129512
7 8 9 10 11 12
0.047629677 0.182377082 -0.076149760 0.193929509 -0.263738039 0.017262360
13 14 15 16 17 18
0.034963597 -0.150127724 0.031011246 -0.040876111 -0.003082873 0.230163158
19 20 21 22 23 24
-0.132062786 0.102436967 0.224414111 0.127249985 -0.155021432 0.121882375
25 26 27 28 29 30
-0.069346201 0.013626435 0.148367290 0.224981324 0.307730373 -0.175126791
31 32 33 34 35 36
-0.254435210 -0.259577691 -0.169528397 -0.160168996 0.225864185 -0.206166978
37 38 39 40 41 42
-0.232084381 -0.050372189 -0.023712400 -0.193655995 -0.045446433 -0.167040796
43 44 45 46 47 48
0.187539695 0.162414615 0.021264046 -0.161010499 0.192895286 0.067022244
49 50 51 52 53 54
0.192909305 0.243136104 -0.024656179 0.037972922 -0.098121913 -0.044125083
55 56
0.151328624 -0.187650973
> postscript(file="/var/www/html/rcomp/tmp/6va7g1258486958.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 0.073557681 NA
1 -0.056262626 0.073557681
2 -0.131009956 -0.056262626
3 -0.028422141 -0.131009956
4 -0.161079153 -0.028422141
5 0.156129512 -0.161079153
6 0.047629677 0.156129512
7 0.182377082 0.047629677
8 -0.076149760 0.182377082
9 0.193929509 -0.076149760
10 -0.263738039 0.193929509
11 0.017262360 -0.263738039
12 0.034963597 0.017262360
13 -0.150127724 0.034963597
14 0.031011246 -0.150127724
15 -0.040876111 0.031011246
16 -0.003082873 -0.040876111
17 0.230163158 -0.003082873
18 -0.132062786 0.230163158
19 0.102436967 -0.132062786
20 0.224414111 0.102436967
21 0.127249985 0.224414111
22 -0.155021432 0.127249985
23 0.121882375 -0.155021432
24 -0.069346201 0.121882375
25 0.013626435 -0.069346201
26 0.148367290 0.013626435
27 0.224981324 0.148367290
28 0.307730373 0.224981324
29 -0.175126791 0.307730373
30 -0.254435210 -0.175126791
31 -0.259577691 -0.254435210
32 -0.169528397 -0.259577691
33 -0.160168996 -0.169528397
34 0.225864185 -0.160168996
35 -0.206166978 0.225864185
36 -0.232084381 -0.206166978
37 -0.050372189 -0.232084381
38 -0.023712400 -0.050372189
39 -0.193655995 -0.023712400
40 -0.045446433 -0.193655995
41 -0.167040796 -0.045446433
42 0.187539695 -0.167040796
43 0.162414615 0.187539695
44 0.021264046 0.162414615
45 -0.161010499 0.021264046
46 0.192895286 -0.161010499
47 0.067022244 0.192895286
48 0.192909305 0.067022244
49 0.243136104 0.192909305
50 -0.024656179 0.243136104
51 0.037972922 -0.024656179
52 -0.098121913 0.037972922
53 -0.044125083 -0.098121913
54 0.151328624 -0.044125083
55 -0.187650973 0.151328624
56 NA -0.187650973
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.056262626 0.073557681
[2,] -0.131009956 -0.056262626
[3,] -0.028422141 -0.131009956
[4,] -0.161079153 -0.028422141
[5,] 0.156129512 -0.161079153
[6,] 0.047629677 0.156129512
[7,] 0.182377082 0.047629677
[8,] -0.076149760 0.182377082
[9,] 0.193929509 -0.076149760
[10,] -0.263738039 0.193929509
[11,] 0.017262360 -0.263738039
[12,] 0.034963597 0.017262360
[13,] -0.150127724 0.034963597
[14,] 0.031011246 -0.150127724
[15,] -0.040876111 0.031011246
[16,] -0.003082873 -0.040876111
[17,] 0.230163158 -0.003082873
[18,] -0.132062786 0.230163158
[19,] 0.102436967 -0.132062786
[20,] 0.224414111 0.102436967
[21,] 0.127249985 0.224414111
[22,] -0.155021432 0.127249985
[23,] 0.121882375 -0.155021432
[24,] -0.069346201 0.121882375
[25,] 0.013626435 -0.069346201
[26,] 0.148367290 0.013626435
[27,] 0.224981324 0.148367290
[28,] 0.307730373 0.224981324
[29,] -0.175126791 0.307730373
[30,] -0.254435210 -0.175126791
[31,] -0.259577691 -0.254435210
[32,] -0.169528397 -0.259577691
[33,] -0.160168996 -0.169528397
[34,] 0.225864185 -0.160168996
[35,] -0.206166978 0.225864185
[36,] -0.232084381 -0.206166978
[37,] -0.050372189 -0.232084381
[38,] -0.023712400 -0.050372189
[39,] -0.193655995 -0.023712400
[40,] -0.045446433 -0.193655995
[41,] -0.167040796 -0.045446433
[42,] 0.187539695 -0.167040796
[43,] 0.162414615 0.187539695
[44,] 0.021264046 0.162414615
[45,] -0.161010499 0.021264046
[46,] 0.192895286 -0.161010499
[47,] 0.067022244 0.192895286
[48,] 0.192909305 0.067022244
[49,] 0.243136104 0.192909305
[50,] -0.024656179 0.243136104
[51,] 0.037972922 -0.024656179
[52,] -0.098121913 0.037972922
[53,] -0.044125083 -0.098121913
[54,] 0.151328624 -0.044125083
[55,] -0.187650973 0.151328624
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.056262626 0.073557681
2 -0.131009956 -0.056262626
3 -0.028422141 -0.131009956
4 -0.161079153 -0.028422141
5 0.156129512 -0.161079153
6 0.047629677 0.156129512
7 0.182377082 0.047629677
8 -0.076149760 0.182377082
9 0.193929509 -0.076149760
10 -0.263738039 0.193929509
11 0.017262360 -0.263738039
12 0.034963597 0.017262360
13 -0.150127724 0.034963597
14 0.031011246 -0.150127724
15 -0.040876111 0.031011246
16 -0.003082873 -0.040876111
17 0.230163158 -0.003082873
18 -0.132062786 0.230163158
19 0.102436967 -0.132062786
20 0.224414111 0.102436967
21 0.127249985 0.224414111
22 -0.155021432 0.127249985
23 0.121882375 -0.155021432
24 -0.069346201 0.121882375
25 0.013626435 -0.069346201
26 0.148367290 0.013626435
27 0.224981324 0.148367290
28 0.307730373 0.224981324
29 -0.175126791 0.307730373
30 -0.254435210 -0.175126791
31 -0.259577691 -0.254435210
32 -0.169528397 -0.259577691
33 -0.160168996 -0.169528397
34 0.225864185 -0.160168996
35 -0.206166978 0.225864185
36 -0.232084381 -0.206166978
37 -0.050372189 -0.232084381
38 -0.023712400 -0.050372189
39 -0.193655995 -0.023712400
40 -0.045446433 -0.193655995
41 -0.167040796 -0.045446433
42 0.187539695 -0.167040796
43 0.162414615 0.187539695
44 0.021264046 0.162414615
45 -0.161010499 0.021264046
46 0.192895286 -0.161010499
47 0.067022244 0.192895286
48 0.192909305 0.067022244
49 0.243136104 0.192909305
50 -0.024656179 0.243136104
51 0.037972922 -0.024656179
52 -0.098121913 0.037972922
53 -0.044125083 -0.098121913
54 0.151328624 -0.044125083
55 -0.187650973 0.151328624
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7tgai1258486958.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8tmy41258486958.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ers81258486958.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10pxwf1258486958.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11mtxr1258486958.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12whe31258486958.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13c0ff1258486959.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14g2lx1258486959.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15v1ru1258486959.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16cgua1258486959.tab")
+ }
>
> system("convert tmp/1j9n01258486958.ps tmp/1j9n01258486958.png")
> system("convert tmp/2fjhh1258486958.ps tmp/2fjhh1258486958.png")
> system("convert tmp/33y1e1258486958.ps tmp/33y1e1258486958.png")
> system("convert tmp/4fyy41258486958.ps tmp/4fyy41258486958.png")
> system("convert tmp/5wyl61258486958.ps tmp/5wyl61258486958.png")
> system("convert tmp/6va7g1258486958.ps tmp/6va7g1258486958.png")
> system("convert tmp/7tgai1258486958.ps tmp/7tgai1258486958.png")
> system("convert tmp/8tmy41258486958.ps tmp/8tmy41258486958.png")
> system("convert tmp/9ers81258486958.ps tmp/9ers81258486958.png")
> system("convert tmp/10pxwf1258486958.ps tmp/10pxwf1258486958.png")
>
>
> proc.time()
user system elapsed
2.397 1.585 4.472